Nonconvex Compressed Sensing and Error Correction

Abstract

The theory of compressed sensing has shown that sparse signals can be reconstructed exactly from remarkably few measurements. In this paper we consider a nonconvex extension. In the context of sparse error correction, we perform numerical experiments that show that for a fixed number of measurements, errors of larger support can be corrected in the nonconvex case. We also provide a theoretical justification for why this should be so.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA490586

Entities

People

  • Rick Chartrand

Organizations

  • Los Alamos National Laboratory

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Algorithms
  • Compressed Sensing
  • Computational Complexity
  • Equations
  • Image Reconstruction
  • Inequalities
  • Information Operations
  • Mathematics
  • Measurement
  • Normal Distribution
  • Probability
  • Sensor Networks
  • Signal Processing
  • Standards

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.